A Python package for post-processing FreeSurfer outputs.
To perform exciting cross-modality comparisons with BOLD and structural data,
you can combine tabular outputs from freesurfer-post
and XCP-D.
Here we will combine ReHo estimates with the surface stats for the Schaefer 100 parcellation.
In XCP-D the Schaefer atlases are included in the 4S
parcellations.
In 4S
parcellations, the Schaefer parcellations are combined with 56 subcortical regions.
Therefore to get the Schaefer 100 parcellation we need to look for the seg-4S156Parcels
files from XCP-D.
In this example we'll use sub-01_task-emotion_dir-LR_run-1_space-fsLR_seg-4S156Parcels_stat-reho_bold.tsv
.
In freesurfer-post
the parcellations come directly from the FreeSurfer annot files in the CBIG repo.
Following those naming conventions, we want to use atlas-Schaefer2018100Parcels7Networks
.
Here we'll use sub-01_atlas-Schaefer2018100Parcels7Networks_surfacestats.tsv
.
Here we combine the two tsvs using Python
import pandas as pd
# Load the tsvs into dataframes.
fspost_data = pd.read_csv(
"sub-01_atlas-Schaefer2018100Parcels7Networks_surfacestats.tsv",
sep = "\t",
)
xcpd_reho = pd.read_csv(
"sub-01_task-emotion_dir-LR_run-1_space-fsLR_seg-4S156Parcels_stat-reho_bold.tsv",
sep = "\t",
)
# Convert xcpd_reho from wide to long format
xcpd_reho_long = pd.melt(
xcpd_reho,
var_name="StructName",
value_name="reho",
)
# Prepend the string "7Networks_" to match the annot StructName
xcpd_reho_long["StructName"] = "7Networks_" + xcpd_reho_long["StructName"]
# Merge fspost_data with xcpd_reho_long, keeping all rows from both datasets
merged_data = pd.merge(fspost_data, xcpd_reho_long, on="StructName")
To do the same thing in R
library(tidyverse)
# Load the tsvs into dataframes.
fspost_data <- read.csv(
"sub-01_atlas-Schaefer2018100Parcels7Networks_surfacestats.tsv",
sep = "\t"
)
xcpd_reho <- read.csv(
"sub-01_task-emotion_dir-LR_run-1_space-fsLR_seg-4S156Parcels_stat-reho_bold.tsv",
sep = "\t"
)
# Convert xcpd_reho from wide to long format
xcpd_reho_long <- xcpd_reho %>%
pivot_longer(cols = everything(),
names_to = "StructName",
values_to = "reho") %>%
mutate(StructName = paste0("7Networks_", StructName))
# Merge fspost_data with xcpd_reho_long, keeping all rows from both datasets
merged_data <- merge(fspost_data, xcpd_reho_long, by = "StructName")
In both cases you will end up with a merged_data
dataframe with 100 rows,
containing a reho
column and all the surface properties.
Assuming you ran an fmriprep with --anat-only
you can use just the
outputs as the inputs for freesurfer-post
.
inputs/data//sourcedata/freesurfer
input_datasets:
fmriprep_anat:
required_files:
- "*fmriprep_anat*.zip"
is_zipped: true
origin_url: "ria+file:///path/to/fmriprep_anat/output_ria#~data"
unzipped_path_containing_subject_dirs: "fmriprep_anat"
path_in_babs: inputs/data/fmriprep_anat
# Arguments in `singularity run`:
bids_app_args:
$SUBJECT_SELECTION_FLAG: "--subject-id"
$SESSION_SELECTION_FLAG: "--session-id"
-w: "$BABS_TMPDIR"
--subjects-dir: "${PWD}/inputs/data/fmriprep_anat/fmriprep_anat/sourcedata/freesurfer"
--fs-license-file: "/path/to/FreeSurfer/license.txt" # [FIX ME] path to
git clone https://github.com/yourusername/freesurfer-post.git
cd freesurfer-post
pip install -e .
git clone https://github.com/yourusername/freesurfer-post.git
cd freesurfer-post
pip install -e ".[dev]"
The package provides a command line interface through the freesurfer-post
command:
# Show help
freesurfer-post --help
# Process FreeSurfer data
freesurfer-post /path/to/subjects_dir /path/to/output participant --subject-id sub-01
# Process a specific session
freesurfer-post /path/to/subjects_dir /path/to/output --subject-id sub-01 --session-id ses-01
This project is licensed under the BSD License - see the LICENSE file for details.